Data augmentation.
Data augmentation operations, such as flipping, rotation, scaling, cropping, contrast, translation, adding gaussian noise etc., are among the most popular techniques for training deep neural networks and improving the generalization capabilities of models. However, in real world, natural data can still exist in a variety of conditions that cannot be accounted for by simple strategies. For instance, the task of identifying the landscape in images can range from rivers, blue sky, freezing tundras to grasslands, forests, etc. Thus, some works [1]}, [2]} have proposed to generate effects such as different seasons artificially to augment the dataset. In this paper, we focus on recognizing natural objects like cat, dog, car, people, etc. wherein we initially discern the most important parts from an object, then use cut and paste inspired from CutMix to generate a new image which helps the networks better attend to the local regions of an image.
